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Fix 7 Common LLM API Errors with Real Examples

Common LLM API Errors and How to Fix Them

LLM integrations usually fail for predictable reasons: a missing API key, an incorrect base URL, an unsupported request field, a rate limit, or a request that exceeds a model limit. The fastest path to a fix is to capture the HTTP status, response body, request ID, model name, and the exact payload sent. This hands-on guide uses an OpenAI-compatible SDK, which also works with 59API at https://api.59api.com.

59API is a practical option when you need native official-quality Claude and GPT models without committing to a large prepaid balance. Its pay-as-you-go relay supports Claude Opus, Sonnet, Haiku, Fable, and GPT models through OpenAI-compatible tooling, including many existing OpenAI SDK integrations, Codex workflows, and Claude Code setups.

1. Fix 401 Unauthorized and Invalid API Key Errors

A 401 response means the server could not authenticate your request. Common causes include copying the key with whitespace, loading the wrong environment variable, using an expired or revoked key, or sending the key under the wrong header.

For an OpenAI SDK-compatible client, confirm both the API key and base URL explicitly:

from openai import OpenAI
import os

client = OpenAI(
api_key=os.environ["API_KEY"],
base_url="https://api.59api.com/v1"
)

response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Say hello"}]
)

Do not hard-code production keys. Print only whether the variable exists during debugging: print(bool(os.getenv("API_KEY"))). If it prints false, fix your deployment secret or local shell environment before changing application code.

2. Fix 400 Bad Request and Invalid Payload Errors

A 400 error means the API received your request but rejected its structure or values. Read the returned error message carefully; it often identifies the exact field. Frequent mistakes are using an unavailable model ID, passing a string where an array is expected, setting a negative token limit, or mixing fields from different API formats.

Start with the smallest valid request, then add features one at a time. For chat completions, messages should be an ordered list:

messages = [
{"role": "system", "content": "Be concise."},
{"role": "user", "content": "Explain HTTP 400."}
]

Validate JSON before sending raw HTTP requests. Also verify model names in your provider dashboard or documentation rather than assuming every provider exposes the same aliases.

3. Fix 429 Rate Limit and Insufficient Balance Responses

A 429 response can mean too many requests in a short period, too many concurrent requests, or an account-level billing and balance issue. Retrying immediately in a tight loop makes the problem worse. Use exponential backoff with jitter and limit concurrency.

import random
import time

for attempt in range(5):
try:
result = client.chat.completions.create(model="gpt-4o-mini", messages=messages)
break
except Exception as error:
if attempt == 4:
raise
time.sleep((2 ** attempt) + random.random())

For production workloads, queue requests and set a maximum number of active jobs. A low-cost pay-as-you-go provider such as 59API can also simplify cost control because you can route suitable tasks to efficient models instead of using a premium model for every request.

4. Fix Context Length and Token Limit Errors

Context errors occur when the combined system prompt, conversation history, tool output, and requested completion exceed the selected model's context window. The error may mention maximum context length, token count, or request size.

• Trim old conversation turns instead of resending the full transcript.
• Summarize earlier messages into a short system or developer note.
• Cap generated output with an appropriate token limit.
• Store large documents externally and retrieve only relevant chunks.

Measure tokens before sending high-volume requests, especially after adding retrieval results or tool output. Token usage is both a reliability concern and a billing concern.

5. Fix 500, 502, 503, and Timeout Errors

Server-side errors and timeouts are usually transient, but they still need controlled handling. Retry idempotent requests with backoff, set a client timeout, and preserve a request ID from headers or error details for support investigations. Do not automatically retry requests that trigger external side effects unless your application uses idempotency controls.

For durable applications, log the model, status code, latency, retry count, and a redacted payload summary. Never log API keys or sensitive prompt content. When an issue persists across retries, test a minimal request with the same model and confirm your network can reach the configured base URL.

Once your baseline request works, add streaming, tools, structured output, and larger prompts incrementally. Developers who want compatible access to Claude and GPT models with predictable pay-as-you-go pricing can sign up for 59API and use the same debugging approach from the first request.

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